Set path Laura: ONLY USE FOR LAURA
# base_path <- "//home.kt.ktzh.ch/B117T23$/Desktop/Riskktaking/Data"
base_path <- "/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/Data_S3"
Library
library(tidyverse)
library(ggplot2)
library(specr)
library(specr)
library(readxl)
library(ggthemes)
library(cowplot)
library(dplyr)
library(knitr)
library(kableExtra)
library(Hmisc)
remotes::install_github("masurp/specr")
Load all data
# Combined GPS WVS
risktaking <- read.csv(file.path(base_path, "gps_wvs_combined.csv"), header=TRUE, as.is=TRUE)
# Umbenennen von 'isocode' zu 'COUNTRY' in risktaking
risktaking <- risktaking %>%
rename(COUNTRY = isocode)
risktaking <- risktaking %>%
select(-hardship_index, -worldmap, -source)
hardship_hs <- read.csv(file.path(base_path, "hardship_HS.csv"))
hardship_finance <- read.csv(file.path(base_path, "hardship_finance.csv"))
hardship_crime <- read.csv(file.path(base_path, "hardship_crime.csv"))
hardship_environment <- read.csv(file.path(base_path, "hardship_environment.csv"))
# Entfernen der nicht benötigten Spalten aus den Datensätzen vor dem Zusammenführen
hardship_hs <- hardship_hs %>%
select(-country, -avg_risktaking)
hardship_finance <- hardship_finance %>%
select(-country, -avg_risktaking)
hardship_crime <- hardship_crime %>%
select(-country, -avg_risktaking)
hardship_environment <- hardship_environment %>%
select(-country, -avg_risktaking)
# Zusammenführen der Datensätze
hardship_combined <- risktaking %>%
left_join(hardship_hs, by = "COUNTRY") %>%
left_join(hardship_finance, by = "COUNTRY") %>%
left_join(hardship_crime, by = "COUNTRY") %>%
left_join(hardship_environment, by = "COUNTRY")
# Überprüfung der Struktur des kombinierten Datensatzes
#str(hardship_combined)
Calculate correlation with risktaking
# Berechnung der Korrelation zwischen risktaking und den anderen Variablen
correlation_results <- cor(hardship_combined[, sapply(hardship_combined, is.numeric)], use = "complete.obs")
# Korrelationstabelle für risktaking extrahieren
risktaking_correlations <- correlation_results["risktaking", ]
# Konvertierung der Korrelationsergebnisse in ein formatiertes Datenframe
correlation_table <- data.frame(
Variable = names(risktaking_correlations),
Correlation = risktaking_correlations
)
# Entfernen der Korrelation von risktaking mit sich selbst
correlation_table <- correlation_table[correlation_table$Variable != "risktaking", ]
# Sortieren der Ergebnisse nach dem Betrag der Korrelation, absteigend
correlation_table <- correlation_table[order(-abs(correlation_table$Correlation)), ]
# Anzeigen der Tabelle
kable(correlation_table, caption = "Korrelationen zwischen risktaking und anderen Variablen") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
Korrelationen zwischen risktaking und anderen Variablen
|
|
Variable
|
Correlation
|
|
age
|
age
|
-0.2381263
|
|
e_exp_watersanithyg100k
|
e_exp_watersanithyg100k
|
0.1305587
|
|
HS_original_infantmortality
|
HS_original_infantmortality
|
0.1298944
|
|
hardship_Finance_index
|
hardship_Finance_index
|
0.1255989
|
|
HS_original_lifeexpectancy
|
HS_original_lifeexpectancy
|
0.1244475
|
|
gender
|
gender
|
-0.1201418
|
|
e_oth_safewater
|
e_oth_safewater
|
0.1159199
|
|
f_original_gini
|
f_original_gini
|
0.1127801
|
|
e_original_gini
|
e_original_gini
|
0.1127801
|
|
hardship_HS
|
hardship_HS
|
0.1122306
|
|
hardship_Crime_index
|
hardship_Crime_index
|
0.1043428
|
|
e_exp_airdeath100k
|
e_exp_airdeath100k
|
0.0982860
|
|
e_oth_drinkingwater
|
e_oth_drinkingwater
|
0.0974426
|
|
HS_oth_cleancooking
|
HS_oth_cleancooking
|
0.0967826
|
|
f_original_gdp
|
f_original_gdp
|
0.0955940
|
|
c_bh_violextchildprot
|
c_bh_violextchildprot
|
0.0936960
|
|
e_exp_watersanithyg
|
e_exp_watersanithyg
|
0.0926370
|
|
e_ses_gini
|
e_ses_gini
|
0.0912298
|
|
c_bh_childmalt
|
c_bh_childmalt
|
0.0876729
|
|
HS_alc_tax_wine
|
HS_alc_tax_wine
|
0.0867029
|
|
c_original_homiciderate
|
c_original_homiciderate
|
0.0826330
|
|
c_bh_homicide
|
c_bh_homicide
|
0.0814154
|
|
c_bh_elderabuse
|
c_bh_elderabuse
|
0.0796172
|
|
HS_drg_treatment
|
HS_drg_treatment
|
0.0779335
|
|
f_hs_oopexp10
|
f_hs_oopexp10
|
0.0754779
|
|
f_eco_cpi
|
f_eco_cpi
|
0.0751496
|
|
c_oth_polstab
|
c_oth_polstab
|
0.0744124
|
|
HS_sex_gini
|
HS_sex_gini
|
0.0728827
|
|
f_eco_gdpdefl_linked
|
f_eco_gdpdefl_linked
|
0.0726808
|
|
HS_alc_roaddeath
|
HS_alc_roaddeath
|
0.0699059
|
|
c_bh_inthomicide
|
c_bh_inthomicide
|
0.0649434
|
|
HS_nic_affordability
|
HS_nic_affordability
|
0.0641391
|
|
c_theft_estcorruption
|
c_theft_estcorruption
|
0.0559263
|
|
f_oth_insfinsvcs_int
|
f_oth_insfinsvcs_int
|
0.0538295
|
|
HS_alc_agelim
|
HS_alc_agelim
|
-0.0533055
|
|
HS_original_genderequality
|
HS_original_genderequality
|
0.0496918
|
|
HS_sex_antiretroviral
|
HS_sex_antiretroviral
|
0.0495593
|
|
e_ses_school
|
e_ses_school
|
0.0425655
|
|
c_bh_parviolenceprog
|
c_bh_parviolenceprog
|
0.0421859
|
|
HS_oth_obesity
|
HS_oth_obesity
|
0.0363355
|
|
HS_mh_mhhospit
|
HS_mh_mhhospit
|
0.0209093
|
|
HS_mh_policy
|
HS_mh_policy
|
-0.0149841
|
|
f_inv_acctownership_primaryedu
|
f_inv_acctownership_primaryedu
|
0.0114821
|
|
e_exp_disaster
|
e_exp_disaster
|
-0.0013405
|
Table with Correlation hardship factors and risktaking
# Laden notwendiger Bibliotheken
library(Hmisc)
library(kableExtra)
# Auswahl aller numerischen Variablen
numeric_vars <- hardship_combined %>%
select(where(is.numeric))
# Berechnen der Korrelationsmatrix und der p-Werte
cor_results <- rcorr(as.matrix(numeric_vars))
# Korrelationen und p-Werte spezifisch für 'risktaking' extrahieren
correlations <- cor_results$r[, "risktaking"] # Korrelationen zu 'risktaking'
p_values <- cor_results$P[, "risktaking"] # p-Werte zu 'risktaking'
# Datenrahmen für die Darstellung erstellen
cor_table <- data.frame(
Variable = rownames(cor_results$r), # Namen der Variablen
Correlation = round(correlations, 5), # Korrelationswerte, gerundet auf 5 Dezimalstellen
P_value = format(p_values, scientific = TRUE), # p-Werte in wissenschaftlicher Notation
Significant = ifelse(p_values < 0.05, "Yes", "No") # Signifikanzflag, basierend auf p-Wert
)
# Tabellendarstellung mit 'kable' und 'kableExtra'
cor_table %>%
kable("html", caption = "Correlations with Risktaking: Summary of Results") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
column_spec(2, bold = TRUE) %>%
column_spec(3, background = "lightyellow")
Correlations with Risktaking: Summary of Results
|
|
Variable
|
Correlation
|
P_value
|
Significant
|
|
gender
|
gender
|
-0.11984
|
0.000000e+00
|
Yes
|
|
age
|
age
|
-0.24184
|
0.000000e+00
|
Yes
|
|
risktaking
|
risktaking
|
1.00000
|
NA
|
NA
|
|
HS_alc_agelim
|
HS_alc_agelim
|
-0.05364
|
0.000000e+00
|
Yes
|
|
HS_alc_tax_wine
|
HS_alc_tax_wine
|
0.08408
|
0.000000e+00
|
Yes
|
|
HS_alc_roaddeath
|
HS_alc_roaddeath
|
0.07748
|
0.000000e+00
|
Yes
|
|
HS_drg_treatment
|
HS_drg_treatment
|
0.07606
|
0.000000e+00
|
Yes
|
|
HS_nic_affordability
|
HS_nic_affordability
|
0.07479
|
0.000000e+00
|
Yes
|
|
HS_mh_policy
|
HS_mh_policy
|
-0.00730
|
5.432256e-04
|
Yes
|
|
HS_sex_gini
|
HS_sex_gini
|
0.08042
|
0.000000e+00
|
Yes
|
|
HS_oth_obesity
|
HS_oth_obesity
|
0.01722
|
4.440892e-16
|
Yes
|
|
HS_oth_cleancooking
|
HS_oth_cleancooking
|
0.10040
|
0.000000e+00
|
Yes
|
|
HS_mh_mhhospit
|
HS_mh_mhhospit
|
0.03081
|
0.000000e+00
|
Yes
|
|
HS_sex_antiretroviral
|
HS_sex_antiretroviral
|
0.05036
|
0.000000e+00
|
Yes
|
|
HS_original_infantmortality
|
HS_original_infantmortality
|
0.14188
|
0.000000e+00
|
Yes
|
|
HS_original_lifeexpectancy
|
HS_original_lifeexpectancy
|
0.13508
|
0.000000e+00
|
Yes
|
|
HS_original_genderequality
|
HS_original_genderequality
|
0.05193
|
0.000000e+00
|
Yes
|
|
hardship_HS
|
hardship_HS
|
0.11857
|
0.000000e+00
|
Yes
|
|
f_inv_acctownership_primaryedu
|
f_inv_acctownership_primaryedu
|
0.02165
|
0.000000e+00
|
Yes
|
|
f_oth_insfinsvcs_int
|
f_oth_insfinsvcs_int
|
0.05273
|
0.000000e+00
|
Yes
|
|
f_hs_oopexp10
|
f_hs_oopexp10
|
0.07968
|
0.000000e+00
|
Yes
|
|
f_eco_gdpdefl_linked
|
f_eco_gdpdefl_linked
|
0.07069
|
0.000000e+00
|
Yes
|
|
f_eco_cpi
|
f_eco_cpi
|
0.10108
|
0.000000e+00
|
Yes
|
|
f_original_gdp
|
f_original_gdp
|
0.10264
|
0.000000e+00
|
Yes
|
|
f_original_gini
|
f_original_gini
|
0.11561
|
0.000000e+00
|
Yes
|
|
hardship_Finance_index
|
hardship_Finance_index
|
0.13907
|
0.000000e+00
|
Yes
|
|
c_bh_homicide
|
c_bh_homicide
|
0.09742
|
0.000000e+00
|
Yes
|
|
c_bh_childmalt
|
c_bh_childmalt
|
0.09575
|
0.000000e+00
|
Yes
|
|
c_bh_violextchildprot
|
c_bh_violextchildprot
|
0.09740
|
0.000000e+00
|
Yes
|
|
c_bh_parviolenceprog
|
c_bh_parviolenceprog
|
0.04102
|
0.000000e+00
|
Yes
|
|
c_bh_elderabuse
|
c_bh_elderabuse
|
0.08784
|
0.000000e+00
|
Yes
|
|
c_bh_inthomicide
|
c_bh_inthomicide
|
0.08021
|
0.000000e+00
|
Yes
|
|
c_theft_estcorruption
|
c_theft_estcorruption
|
0.06759
|
0.000000e+00
|
Yes
|
|
c_oth_polstab
|
c_oth_polstab
|
0.08484
|
0.000000e+00
|
Yes
|
|
c_original_homiciderate
|
c_original_homiciderate
|
0.09861
|
0.000000e+00
|
Yes
|
|
hardship_Crime_index
|
hardship_Crime_index
|
0.11775
|
0.000000e+00
|
Yes
|
|
e_original_gini
|
e_original_gini
|
0.11728
|
0.000000e+00
|
Yes
|
|
e_oth_drinkingwater
|
e_oth_drinkingwater
|
0.10202
|
0.000000e+00
|
Yes
|
|
e_oth_safewater
|
e_oth_safewater
|
0.12215
|
0.000000e+00
|
Yes
|
|
e_exp_watersanithyg100k
|
e_exp_watersanithyg100k
|
0.13797
|
0.000000e+00
|
Yes
|
|
e_ses_gini
|
e_ses_gini
|
0.09593
|
0.000000e+00
|
Yes
|
|
e_ses_school
|
e_ses_school
|
0.04516
|
0.000000e+00
|
Yes
|
|
e_exp_disaster
|
e_exp_disaster
|
-0.01658
|
3.996803e-15
|
Yes
|
|
e_exp_airdeath100k
|
e_exp_airdeath100k
|
0.11031
|
0.000000e+00
|
Yes
|
|
e_exp_watersanithyg
|
e_exp_watersanithyg
|
0.09126
|
0.000000e+00
|
Yes
|
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("HS_alc_agelim", "HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral", "HS_original_infantmortality",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS", "f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index",
"c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_bh_inthomicide",
"c_theft_estcorruption", "c_oth_polstab", "c_original_homiciderate",
"hardship_Crime_index", "e_original_gini", "e_oth_drinkingwater",
"e_oth_safewater", "e_exp_watersanithyg100k", "e_ses_gini",
"e_ses_school", "e_exp_disaster", "e_exp_airdeath100k",
"e_exp_watersanithyg"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 42
##
## Specifications:
##
## Independent variable: HS_alc_agelim, HS_alc_tax_wine, HS_alc_roaddeath, HS_drg_treatment, HS_nic_affordability, HS_mh_policy, HS_sex_gini, HS_oth_obesity, HS_oth_cleancooking, HS_mh_mhhospit, HS_sex_antiretroviral, HS_original_infantmortality, HS_original_lifeexpectancy, HS_original_genderequality, hardship_HS, f_inv_acctownership_primaryedu, f_oth_insfinsvcs_int, f_hs_oopexp10, f_eco_gdpdefl_linked, f_eco_cpi, f_original_gdp, f_original_gini, hardship_Finance_index, c_bh_homicide, c_bh_childmalt, c_bh_violextchildprot, c_bh_parviolenceprog, c_bh_elderabuse, c_bh_inthomicide, c_theft_estcorruption, c_oth_polstab, c_original_homiciderate, hardship_Crime_index, e_original_gini, e_oth_drinkingwater, e_oth_safewater, e_exp_watersanithyg100k, e_ses_gini, e_ses_school, e_exp_disaster, e_exp_airdeath100k, e_exp_watersanithyg
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x1444b6940>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 HS_alc_agelim risktaking lm no covariates all risktaking ~ HS_a…
## 2 HS_alc_tax_wine risktaking lm no covariates all risktaking ~ HS_a…
## 3 HS_alc_roaddeath risktaking lm no covariates all risktaking ~ HS_a…
## 4 HS_drg_treatment risktaking lm no covariates all risktaking ~ HS_d…
## 5 HS_nic_affordability risktaking lm no covariates all risktaking ~ HS_n…
## 6 HS_mh_policy risktaking lm no covariates all risktaking ~ HS_m…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 42 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.91 0.33 -0.58 2.46 0.68 1.12
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 7.033 sec elapsed
## Number of specifications: 42
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.91313 0.32865 -0.57659 2.46013 0.67921 1.12087
##
## Descriptive summary of sample sizes:
##
## median min max
## 224583 221536 225551
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_agel… risk… lm no cova… all riskta… -0.577 0.0226 -25.5
## 2 HS_alc_tax_… risk… lm no cova… all riskta… 0.917 0.0229 40.0
## 3 HS_alc_road… risk… lm no cova… all riskta… 0.853 0.0232 36.8
## 4 HS_drg_trea… risk… lm no cova… all riskta… 0.784 0.0217 36.1
## 5 HS_nic_affo… risk… lm no cova… all riskta… 0.806 0.0227 35.5
## 6 HS_mh_policy risk… lm no cova… all riskta… -0.170 0.0491 -3.46
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.913 0.329 -0.577 2.46 0.679 1.12 224583
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 42 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HS_alc_agelim -0.577 -0.577 -0.577 -0.577 224583
## 2 HS_alc_roaddeath 0.853 0.853 0.853 0.853 224583
## 3 HS_alc_tax_wine 0.917 0.917 0.917 0.917 224583
## 4 HS_drg_treatment 0.784 0.784 0.784 0.784 224583
## 5 HS_mh_mhhospit 0.327 0.327 0.327 0.327 224583
## 6 HS_mh_policy -0.170 -0.170 -0.170 -0.170 224583
## 7 HS_nic_affordability 0.806 0.806 0.806 0.806 224583
## 8 HS_original_genderequality 0.677 0.677 0.677 0.677 224583
## 9 HS_original_infantmortality 1.49 1.49 1.49 1.49 224583
## 10 HS_original_lifeexpectancy 1.40 1.40 1.40 1.40 224583
## # ℹ 32 more rows
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("HS_alc_agelim", "HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral", "HS_original_infantmortality",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS", "f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index",
"c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_bh_inthomicide",
"c_theft_estcorruption", "c_oth_polstab", "c_original_homiciderate",
"hardship_Crime_index", "e_original_gini", "e_oth_drinkingwater",
"e_oth_safewater", "e_exp_watersanithyg100k", "e_ses_gini",
"e_ses_school", "e_exp_disaster", "e_exp_airdeath100k",
"e_exp_watersanithyg"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 3.628 sec elapsed
## Number of specifications: 42
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1 0.37 -0.64 2.8 0.76 1.23
##
## Descriptive summary of sample sizes:
##
## median min max
## 119096 117485 119591
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_agel… risk… lm no cova… all riskta… -0.64 0.03 -19.9
## 2 HS_alc_tax_… risk… lm no cova… all riskta… 0.93 0.03 29.5
## 3 HS_alc_road… risk… lm no cova… all riskta… 1 0.03 31.2
## 4 HS_drg_trea… risk… lm no cova… all riskta… 0.83 0.03 28.6
## 5 HS_nic_affo… risk… lm no cova… all riskta… 0.94 0.03 30.0
## 6 HS_mh_policy risk… lm no cova… all riskta… -0.11 0.07 -1.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("HS_alc_agelim", "HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral", "HS_original_infantmortality",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS", "f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index",
"c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_bh_inthomicide",
"c_theft_estcorruption", "c_oth_polstab", "c_original_homiciderate",
"hardship_Crime_index", "e_original_gini", "e_oth_drinkingwater",
"e_oth_safewater", "e_exp_watersanithyg100k", "e_ses_gini",
"e_ses_school", "e_exp_disaster", "e_exp_airdeath100k",
"e_exp_watersanithyg"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 3.134 sec elapsed
## Number of specifications: 42
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.76 0.29 -0.46 2.04 0.54 0.95
##
## Descriptive summary of sample sizes:
##
## median min max
## 105487 104051 105960
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_agel… risk… lm no cova… all riskta… -0.46 0.03 -14.6
## 2 HS_alc_tax_… risk… lm no cova… all riskta… 0.84 0.03 25.4
## 3 HS_alc_road… risk… lm no cova… all riskta… 0.69 0.03 20.9
## 4 HS_drg_trea… risk… lm no cova… all riskta… 0.7 0.03 21.8
## 5 HS_nic_affo… risk… lm no cova… all riskta… 0.61 0.03 18.9
## 6 HS_mh_policy risk… lm no cova… all riskta… -0.26 0.07 -3.72
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Hardship Health/Safety
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("HS_alc_agelim", "HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral", "HS_original_infantmortality",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 15
##
## Specifications:
##
## Independent variable: HS_alc_agelim, HS_alc_tax_wine, HS_alc_roaddeath, HS_drg_treatment, HS_nic_affordability, HS_mh_policy, HS_sex_gini, HS_oth_obesity, HS_oth_cleancooking, HS_mh_mhhospit, HS_sex_antiretroviral, HS_original_infantmortality, HS_original_lifeexpectancy, HS_original_genderequality, hardship_HS
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x1419ea940>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 HS_alc_agelim risktaking lm no covariates all risktaking ~ HS_a…
## 2 HS_alc_tax_wine risktaking lm no covariates all risktaking ~ HS_a…
## 3 HS_alc_roaddeath risktaking lm no covariates all risktaking ~ HS_a…
## 4 HS_drg_treatment risktaking lm no covariates all risktaking ~ HS_d…
## 5 HS_nic_affordability risktaking lm no covariates all risktaking ~ HS_n…
## 6 HS_mh_policy risktaking lm no covariates all risktaking ~ HS_m…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 15 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.81 0.43 -0.58 2.46 0.47 1
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 2.721 sec elapsed
## Number of specifications: 15
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.80569 0.4252 -0.57659 2.46013 0.47276 1.00453
##
## Descriptive summary of sample sizes:
##
## median min max
## 224583 224583 224583
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_agel… risk… lm no cova… all riskta… -0.577 0.0226 -25.5
## 2 HS_alc_tax_… risk… lm no cova… all riskta… 0.917 0.0229 40.0
## 3 HS_alc_road… risk… lm no cova… all riskta… 0.853 0.0232 36.8
## 4 HS_drg_trea… risk… lm no cova… all riskta… 0.784 0.0217 36.1
## 5 HS_nic_affo… risk… lm no cova… all riskta… 0.806 0.0227 35.5
## 6 HS_mh_policy risk… lm no cova… all riskta… -0.170 0.0491 -3.46
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 0.806 0.425 -0.577 2.46 0.473 1.00 224583
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 15 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HS_alc_agelim -0.577 -0.577 -0.577 -0.577 224583
## 2 HS_alc_roaddeath 0.853 0.853 0.853 0.853 224583
## 3 HS_alc_tax_wine 0.917 0.917 0.917 0.917 224583
## 4 HS_drg_treatment 0.784 0.784 0.784 0.784 224583
## 5 HS_mh_mhhospit 0.327 0.327 0.327 0.327 224583
## 6 HS_mh_policy -0.170 -0.170 -0.170 -0.170 224583
## 7 HS_nic_affordability 0.806 0.806 0.806 0.806 224583
## 8 HS_original_genderequality 0.677 0.677 0.677 0.677 224583
## 9 HS_original_infantmortality 1.49 1.49 1.49 1.49 224583
## 10 HS_original_lifeexpectancy 1.40 1.40 1.40 1.40 224583
## 11 HS_oth_cleancooking 1.09 1.09 1.09 1.09 224583
## 12 HS_oth_obesity 0.169 0.169 0.169 0.169 224583
## 13 HS_sex_antiretroviral 0.618 0.618 0.618 0.618 224583
## 14 HS_sex_gini 0.837 0.837 0.837 0.837 224583
## 15 hardship_HS 2.46 2.46 2.46 2.46 224583
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2.5, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("HS_alc_agelim", "HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral", "HS_original_infantmortality",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.341 sec elapsed
## Number of specifications: 15
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.93 0.53 -0.64 2.8 0.49 1.14
##
## Descriptive summary of sample sizes:
##
## median min max
## 119096 119096 119096
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_agel… risk… lm no cova… all riskta… -0.64 0.03 -19.9
## 2 HS_alc_tax_… risk… lm no cova… all riskta… 0.93 0.03 29.5
## 3 HS_alc_road… risk… lm no cova… all riskta… 1 0.03 31.2
## 4 HS_drg_trea… risk… lm no cova… all riskta… 0.83 0.03 28.6
## 5 HS_nic_affo… risk… lm no cova… all riskta… 0.94 0.03 30.0
## 6 HS_mh_policy risk… lm no cova… all riskta… -0.11 0.07 -1.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("HS_alc_agelim", "HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral", "HS_original_infantmortality",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.238 sec elapsed
## Number of specifications: 15
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.63 0.31 -0.46 1.96 0.4 0.83
##
## Descriptive summary of sample sizes:
##
## median min max
## 105487 105487 105487
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_agel… risk… lm no cova… all riskta… -0.46 0.03 -14.6
## 2 HS_alc_tax_… risk… lm no cova… all riskta… 0.84 0.03 25.4
## 3 HS_alc_road… risk… lm no cova… all riskta… 0.69 0.03 20.9
## 4 HS_drg_trea… risk… lm no cova… all riskta… 0.7 0.03 21.8
## 5 HS_nic_affo… risk… lm no cova… all riskta… 0.61 0.03 18.9
## 6 HS_mh_policy risk… lm no cova… all riskta… -0.26 0.07 -3.72
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Hardship Finance
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 8
##
## Specifications:
##
## Independent variable: f_inv_acctownership_primaryedu, f_oth_insfinsvcs_int, f_hs_oopexp10, f_eco_gdpdefl_linked, f_eco_cpi, f_original_gdp, f_original_gini, hardship_Finance_index
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x14380f1c8>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 f_inv_acctownership_primaryedu risktaking lm no covariates all risktak…
## 2 f_oth_insfinsvcs_int risktaking lm no covariates all risktak…
## 3 f_hs_oopexp10 risktaking lm no covariates all risktak…
## 4 f_eco_gdpdefl_linked risktaking lm no covariates all risktak…
## 5 f_eco_cpi risktaking lm no covariates all risktak…
## 6 f_original_gdp risktaking lm no covariates all risktak…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 8 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.88 0.37 0.22 2.43 0.66 1.12
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.441 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.87506 0.36897 0.22392 2.4322 0.66037 1.11566
##
## Descriptive summary of sample sizes:
##
## median min max
## 225551 221536 225551
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.224 0.0218 10.3
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.506 0.0202 25.1
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.785 0.0207 38.0
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.712 0.0213 33.4
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.965 0.02 48.3
## 6 f_original_… risk… lm no cova… all riskta… 1.14 0.0233 49.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.875 0.369 0.224 2.43 0.660 1.12 225551
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 8 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 f_eco_cpi 0.965 0.965 0.965 0.965 225551
## 2 f_eco_gdpdefl_linked 0.712 0.712 0.712 0.712 221536
## 3 f_hs_oopexp10 0.785 0.785 0.785 0.785 225551
## 4 f_inv_acctownership_primaryedu 0.224 0.224 0.224 0.224 225551
## 5 f_original_gdp 1.14 1.14 1.14 1.14 225551
## 6 f_original_gini 1.11 1.11 1.11 1.11 225551
## 7 f_oth_insfinsvcs_int 0.506 0.506 0.506 0.506 225551
## 8 hardship_Finance_index 2.43 2.43 2.43 2.43 225551
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index"
),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.556 sec elapsed
## Number of specifications: 7
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.05 0.37 0.61 2.73 0.85 1.24
##
## Descriptive summary of sample sizes:
##
## median min max
## 119591 117485 119591
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_oth_insfi… risk… lm no cova… all riskta… 0.61 0.03 21.8
## 2 f_hs_oopexp… risk… lm no cova… all riskta… 0.89 0.03 31.6
## 3 f_eco_gdpde… risk… lm no cova… all riskta… 0.8 0.03 27.7
## 4 f_eco_cpi risk… lm no cova… all riskta… 1.05 0.03 38.7
## 5 f_original_… risk… lm no cova… all riskta… 1.3 0.03 40.6
## 6 f_original_… risk… lm no cova… all riskta… 1.18 0.03 43.4
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index"
),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.618 sec elapsed
## Number of specifications: 7
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.86 0.32 0.38 2.04 0.63 0.94
##
## Descriptive summary of sample sizes:
##
## median min max
## 105960 104051 105960
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_oth_insfi… risk… lm no cova… all riskta… 0.38 0.03 13.4
## 2 f_hs_oopexp… risk… lm no cova… all riskta… 0.64 0.03 21.6
## 3 f_eco_gdpde… risk… lm no cova… all riskta… 0.61 0.03 19.4
## 4 f_eco_cpi risk… lm no cova… all riskta… 0.86 0.03 29.5
## 5 f_original_… risk… lm no cova… all riskta… 0.9 0.03 27.1
## 6 f_original_… risk… lm no cova… all riskta… 0.98 0.03 33.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Hardship Crime
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_bh_inthomicide",
"c_theft_estcorruption", "c_oth_polstab", "c_original_homiciderate",
"hardship_Crime_index"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 10
##
## Specifications:
##
## Independent variable: c_bh_homicide, c_bh_childmalt, c_bh_violextchildprot, c_bh_parviolenceprog, c_bh_elderabuse, c_bh_inthomicide, c_theft_estcorruption, c_oth_polstab, c_original_homiciderate, hardship_Crime_index
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x1442cfc10>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 c_bh_homicide risktaking lm no covariates all risktaking ~ c_b…
## 2 c_bh_childmalt risktaking lm no covariates all risktaking ~ c_b…
## 3 c_bh_violextchildprot risktaking lm no covariates all risktaking ~ c_b…
## 4 c_bh_parviolenceprog risktaking lm no covariates all risktaking ~ c_b…
## 5 c_bh_elderabuse risktaking lm no covariates all risktaking ~ c_b…
## 6 c_bh_inthomicide risktaking lm no covariates all risktaking ~ c_b…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 10 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.94 0.13 0.43 1.73 0.81 1
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.59 sec elapsed
## Number of specifications: 10
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.94298 0.13212 0.43309 1.73386 0.80953 0.99773
##
## Descriptive summary of sample sizes:
##
## median min max
## 225551 225551 225551
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.947 0.0204 46.5
## 2 c_bh_childm… risk… lm no cova… all riskta… 1.01 0.0221 45.7
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.05 0.0227 46.5
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.433 0.0222 19.5
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.939 0.0224 41.9
## 6 c_bh_inthom… risk… lm no cova… all riskta… 0.780 0.0204 38.2
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.943 0.132 0.433 1.73 0.810 0.998 225551
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 10 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 c_bh_childmalt 1.01 1.01 1.01 1.01 225551
## 2 c_bh_elderabuse 0.939 0.939 0.939 0.939 225551
## 3 c_bh_homicide 0.947 0.947 0.947 0.947 225551
## 4 c_bh_inthomicide 0.780 0.780 0.780 0.780 225551
## 5 c_bh_parviolenceprog 0.433 0.433 0.433 0.433 225551
## 6 c_bh_violextchildprot 1.05 1.05 1.05 1.05 225551
## 7 c_original_homiciderate 0.958 0.958 0.958 0.958 225551
## 8 c_oth_polstab 0.897 0.897 0.897 0.897 225551
## 9 c_theft_estcorruption 0.685 0.685 0.685 0.685 225551
## 10 hardship_Crime_index 1.73 1.73 1.73 1.73 225551
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_bh_inthomicide",
"c_theft_estcorruption", "c_oth_polstab", "c_original_homiciderate",
"hardship_Crime_index"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.787 sec elapsed
## Number of specifications: 10
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.01 0.12 0.4 1.87 0.88 1.04
##
## Descriptive summary of sample sizes:
##
## median min max
## 119591 119591 119591
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 1.03 0.03 37.4
## 2 c_bh_childm… risk… lm no cova… all riskta… 1.04 0.03 34.4
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.14 0.03 36.6
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.4 0.03 13.4
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.98 0.03 32.2
## 6 c_bh_inthom… risk… lm no cova… all riskta… 0.85 0.03 30.8
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_bh_inthomicide",
"c_theft_estcorruption", "c_oth_polstab", "c_original_homiciderate",
"hardship_Crime_index"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.782 sec elapsed
## Number of specifications: 10
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.85 0.16 0.42 1.54 0.71 0.91
##
## Descriptive summary of sample sizes:
##
## median min max
## 105960 105960 105960
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.85 0.03 28.5
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.93 0.03 29.1
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.92 0.03 28.4
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.42 0.03 13.0
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.89 0.03 27.2
## 6 c_bh_inthom… risk… lm no cova… all riskta… 0.7 0.03 23.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Hardship Environment
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("e_original_gini", "e_oth_drinkingwater",
"e_oth_safewater", "e_exp_watersanithyg100k", "e_ses_gini",
"e_ses_school", "e_exp_disaster", "e_exp_airdeath100k",
"e_exp_watersanithyg"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 9
##
## Specifications:
##
## Independent variable: e_original_gini, e_oth_drinkingwater, e_oth_safewater, e_exp_watersanithyg100k, e_ses_gini, e_ses_school, e_exp_disaster, e_exp_airdeath100k, e_exp_watersanithyg
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x1336a9d50>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 e_original_gini risktaking lm no covariates all risktaking ~ e…
## 2 e_oth_drinkingwater risktaking lm no covariates all risktaking ~ e…
## 3 e_oth_safewater risktaking lm no covariates all risktaking ~ e…
## 4 e_exp_watersanithyg100k risktaking lm no covariates all risktaking ~ e…
## 5 e_ses_gini risktaking lm no covariates all risktaking ~ e…
## 6 e_ses_school risktaking lm no covariates all risktaking ~ e…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 9 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.13 0.32 -0.18 1.49 0.91 1.23
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.367 sec elapsed
## Number of specifications: 9
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.12536 0.31975 -0.17943 1.4903 0.90969 1.22758
##
## Descriptive summary of sample sizes:
##
## median min max
## 224550 224550 224550
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_original_… risk… lm no cova… all riskta… 1.13 0.0201 56.0
## 2 e_oth_drink… risk… lm no cova… all riskta… 1.23 0.0253 48.6
## 3 e_oth_safew… risk… lm no cova… all riskta… 1.43 0.0244 58.3
## 4 e_exp_water… risk… lm no cova… all riskta… 1.49 0.0226 66.0
## 5 e_ses_gini risk… lm no cova… all riskta… 0.947 0.0207 45.7
## 6 e_ses_school risk… lm no cova… all riskta… 0.580 0.0271 21.4
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1.13 0.320 -0.179 1.49 0.910 1.23 224550
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 9 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 e_exp_airdeath100k 1.13 1.13 1.13 1.13 224550
## 2 e_exp_disaster -0.179 -0.179 -0.179 -0.179 224550
## 3 e_exp_watersanithyg 0.910 0.910 0.910 0.910 224550
## 4 e_exp_watersanithyg100k 1.49 1.49 1.49 1.49 224550
## 5 e_original_gini 1.13 1.13 1.13 1.13 224550
## 6 e_oth_drinkingwater 1.23 1.23 1.23 1.23 224550
## 7 e_oth_safewater 1.43 1.43 1.43 1.43 224550
## 8 e_ses_gini 0.947 0.947 0.947 0.947 224550
## 9 e_ses_school 0.580 0.580 0.580 0.580 224550
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("e_original_gini", "e_oth_drinkingwater",
"e_oth_safewater", "e_exp_watersanithyg100k", "e_ses_gini",
"e_ses_school", "e_exp_disaster", "e_exp_airdeath100k",
"e_exp_watersanithyg"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.725 sec elapsed
## Number of specifications: 9
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.2 0.32 -0.12 1.64 1.05 1.41
##
## Descriptive summary of sample sizes:
##
## median min max
## 119093 119093 119093
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_original_… risk… lm no cova… all riskta… 1.2 0.03 43.8
## 2 e_oth_drink… risk… lm no cova… all riskta… 1.41 0.04 39.8
## 3 e_oth_safew… risk… lm no cova… all riskta… 1.62 0.03 47.9
## 4 e_exp_water… risk… lm no cova… all riskta… 1.64 0.03 52.9
## 5 e_ses_gini risk… lm no cova… all riskta… 1.05 0.03 37.3
## 6 e_ses_school risk… lm no cova… all riskta… 0.66 0.04 17.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("e_original_gini", "e_oth_drinkingwater",
"e_oth_safewater", "e_exp_watersanithyg100k", "e_ses_gini",
"e_ses_school", "e_exp_disaster", "e_exp_airdeath100k",
"e_exp_watersanithyg"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.717 sec elapsed
## Number of specifications: 9
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.96 0.27 -0.22 1.24 0.67 1
##
## Descriptive summary of sample sizes:
##
## median min max
## 105457 105457 105457
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_original_… risk… lm no cova… all riskta… 1 0.03 34.1
## 2 e_oth_drink… risk… lm no cova… all riskta… 0.96 0.04 27.0
## 3 e_oth_safew… risk… lm no cova… all riskta… 1.14 0.03 32.7
## 4 e_exp_water… risk… lm no cova… all riskta… 1.24 0.03 37.9
## 5 e_ses_gini risk… lm no cova… all riskta… 0.8 0.03 26.4
## 6 e_ses_school risk… lm no cova… all riskta… 0.45 0.04 11.9
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
